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Journal of Evolution and Technology.
1998. Vol. 1
When will computer hardware match the human brain?
(Received Dec. 1997)
Hans Moravec
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213−3890, USA
net:
hpm@cmu.edu
web:
http://www.frc.ri.cmu.edu/~hpm/
ABSTRACT
This paper describes how the performance of AI machines tends to improve at the same pace
that AI researchers get access to faster hardware. The processing power and memory capacity
necessary to match general intellectual performance of the human brain are estimated. Based
on extrapolation of past trends and on examination of technologies under development, it is
predicted that the required hardware will be available in cheap machines in the 2020s.
Brains, Eyes and Machines
Computers have far to go to match human strengths, and our estimates will depend on analogy and
extrapolation. Fortunately, these are grounded in the first bit of the journey, now behind us. Thirty years of
computer vision reveals that 1 MIPS can extract simple features from real−time imagery−−tracking a white
line or a white spot on a mottled background. 10 MIPS can follow complex gray−scale patches−−as smart
bombs, cruise missiles and early self−driving vans attest. 100 MIPS can follow moderately unpredictable
features like roads−−as recent long NAVLAB trips demonstrate. 1,000 MIPS will be adequate for
coarse−grained three−dimensional spatial awareness−−illustrated by several mid−resolution stereoscopic
vision programs, including my own. 10,000 MIPS can find three−dimensional objects in clutter−−suggested
by several "bin−picking" and high−resolution stereo−vision demonstrations, which accomplish the task in an
hour or so at 10 MIPS. The data fades there−−research careers are too short, and computer memories too
small, for significantly more elaborate experiments.
There are considerations other than sheer scale. At 1 MIPS the best results come from finely hand−crafted
programs that distill sensor data with utmost efficiency. 100−MIPS processes weigh their inputs against a
wide range of hypotheses, with many parameters, that learning programs adjust better than the overburdened
programmers. Learning of all sorts will be increasingly important as computer power and robot programs
grow. This effect is evident in related areas. At the close of the 1980s, as widely available computers reached
10 MIPS, good optical character reading (OCR) programs, able to read most printed and typewritten text,
began to appear. They used hand−constructed "feature detectors" for parts of letter shapes, with very little
learning. As computer power passed 100 MIPS, trainable OCR programs appeared that could learn unusual
typestyles from examples, and the latest and best programs learn their entire data sets. Handwriting
recognizers, used by the Post Office to sort mail, and in computers, notably Apple's Newton, have followed a
similar path. Speech recognition also fits the model. Under the direction of Raj Reddy, who began his
research at Stanford in the 1960s, Carnegie Mellon has led in computer transcription of continuous spoken
speech. In 1992 Reddy's group demonstrated a program called Sphinx II on a 15−MIPS workstation with 100
MIPS of specialized signal−processing circuitry. Sphinx II was able to deal with arbitrary English speakers
using a several−thousand−word vocabulary. The system's word detectors, encoded in statistical structures
known as Markov tables, were shaped by an automatic learning process that digested hundreds of hours of
spoken examples from thousands of Carnegie Mellon volunteers enticed by rewards of pizza and ice cream.
Several practical voice−control and dictation systems are sold for personal computers today, and some heavy
users are substituting larynx for wrist damage.
More computer power is needed to reach human performance, but how much? Human and animal brain sizes
imply an answer, if we can relate nerve volume to computation. Structurally and functionally, one of the best
understood neural assemblies is the retina of the vertebrate eye. Happily, similar operations have been
developed for robot vision, handing us a rough conversion factor.
The retina is a transparent, paper−thin layer of nerve tissue at the back of the eyeball on which the eye's lens
projects an image of the world. It is connected by the optic nerve, a million−fiber cable, to regions deep in the
brain. It is a part of the brain convenient for study, even in living animals because of its peripheral location
and because its function is straightforward compared with the brain's other mysteries. A human retina is less
than a centimeter square and a half−millimeter thick. It has about 100 million neurons, of five distinct kinds.
Light−sensitive cells feed wide spanning horizontal cells and narrower bipolar cells, which are
interconnected by whose outgoing fibers bundle to form the optic nerve. Each of the million ganglion−cell
axons carries signals from a amacrine cells, and finally ganglion cells, particular patch of image, indicating
light intensity differences over space or time: a million edge and motion detections. Overall, the retina seems
to process about ten one−million−point images per second.
It takes robot vision programs about 100 computer instructions to derive single edge or motion detections
from comparable video images. 100 million instructions are needed to do a million detections, and 1,000
MIPS to repeat them ten times per second to match the retina.
The 1,500 cubic centimeter human brain is about 100,000 times as large as the retina, suggesting that
matching overall human behavior will take about 100 million MIPS of computer power. Computer chess
bolsters this yardstick. Deep Blue, the chess machine that bested world chess champion Garry Kasparov in
1997, used specialized chips to process chess moves at a the speed equivalent to a 3 million MIPS universal
computer (see Figure 3−4). This is 1/30 of the estimate for total human performance. Since it is plausible that
Kasparov, probably the best human player ever, can apply his brainpower to the strange problems of chess
with an efficiency of 1/30, Deep Blue's near parity with Kasparov's chess skill supports the retina−based
extrapolation.
The most powerful experimental supercomputers in 1998, composed of thousands or tens of thousands of the
fastest microprocessors and costing tens of millions of dollars, can do a few million MIPS. They are within
striking distance of being powerful enough to match human brainpower, but are unlikely to be applied to that
end. Why tie up a rare twenty−million−dollar asset to develop one ersatz−human, when millions of
inexpensive original−model humans are available? Such machines are needed for high−value scientific
calculations, mostly physical simulations, having no cheaper substitutes. AI research must wait for the power
to become more affordable.
If 100 million MIPS could do the job of the human brain's 100 billion neurons, then one neuron is worth
about 1/1,000 MIPS, i.e., 1,000 instructions per second. That's probably not enough to simulate an actual
neuron, which can produce 1,000 finely timed pulses per second. Our estimate is for very efficient programs
that imitate the aggregate function of thousand−neuron assemblies. Almost all nervous systems contain
subassemblies that big.
The small nervous systems of insects and other invertebrates seem to be hardwired from birth, each neuron
having its own special predetermined links and function. The few−hundred−million−bit insect genome is
enough to specify connections of each of their hundred thousand neurons. Humans, on the other hand, have
100 billion neurons, but only a few billion bits of genome. The human brain seems to consist largely of
regular structures whose neurons are trimmed away as skills are learned, like featureless marble blocks
chiseled into individual sculptures. Analogously, robot programs were precisely hand−coded when they
occupied only a few hundred thousand bytes of memory. Now that they've grown to tens of millions of bytes,
most of their content is learned from example. But there is a big practical difference between animal and
robot learning. Animals learn individually, but robot learning can be copied from one machine to another.
For instance, today's text and speech understanding programs were painstakingly trained over months or
years, but each customer's copy of the software is "born" fully educated. Decoupling training from use will
allow robots to do more with less. Big computers at the factory−−maybe supercomputers with 1,000 times the
power of machines that can reasonably be placed in a robot−−will process large training sets under careful
human supervision, and distill the results into efficient programs and arrays of settings that are then copied
into myriads of individual robots with more modest processors.
Programs need memory as well as processing speed to do their work. The ratio of memory to speed has
remained constant during computing history. The earliest electronic computers had a few thousand bytes of
memory and could do a few thousand calculations per second. Medium computers of 1980 had a million bytes
of memory and did a million calculations per second. Supercomputers in 1990 did a billion calculations per
second and had a billion bytes of memory. The latest, greatest supercomputers can do a trillion calculations
per second and can have a trillion bytes of memory. Dividing memory by speed defines a "time constant,"
roughly how long it takes the computer to run once through its memory. One megabyte per MIPS gives one
second, a nice human interval. Machines with less memory for their speed, typically new models, seem fast,
but unnecessarily limited to small programs. Models with more memory for their speed, often ones reaching
the end of their run, can handle larger programs, but unpleasantly slowly. For instance, the original
Macintosh was introduced in 1984 with 1/2 MIPS and 1/8 megabyte, and was then considered a very fast
machine. The equally fast "fat Mac" with 1/2 megabyte ran larger programs at tolerable speed, but the 1
megabyte "Mac plus" verged on slow. The four megabyte "Mac classic," the last 1/2 MIPS machine in the
line, was intolerably slow, and was soon supplanted by ten−times−faster processors in the same enclosure.
Customers maintain the ratio by asking "would the next dollar be better spent on more speed or more
memory?"
The best evidence about nervous system memory puts most of it in the synapses connecting the neurons.
Molecular adjustments allow synapses to be in a number of distinguishable states, lets say one byte's worth.
Then the 100−trillion−synapse brain would hold the equivalent 100 million megabytes. This agrees with our
earlier estimate that it would take 100 million MIPS to mimic the brain's function. The megabyte/MIPS ratio
seems to hold for nervous systems too! The contingency is the other way around: computers are configured to
interact at human time scales, and robots interacting with humans seem also to be best at that ratio. On the
other hand, faster machines, for instance audio and video processors and controllers of high−performance
aircraft, have many MIPS for each megabyte. Very slow machines, for instance time−lapse security cameras
and automatic data libraries, store many megabytes for each of their MIPS. Flying insects seem to be a few
times faster than humans, so may have more MIPS than megabytes. As in animals, cells in plants signal one
other electrochemically and enzymatically. Some plant cells seem specialized for communication, though
apparently not as extremely as animal neurons. One day we may find that plants remember much, but process
it slowly (how does a redwood tree manage to rebuff rapidly evolving pests during a 2,000 year lifespan,
when it took mosquitoes only a few decades to overcome DDT?).
With our conversions, a 100−MIPS robot, for instance Navlab, has mental power similar to a
100,000−neuron housefly. The following figure rates various entities.
MIPS and Megabytes.
to mimic their behavior. Note the scale. Entities rated by the computational power and memory of the
smallest universal computer needed is logarithmic on both axes: each vertical division represents a thousandfold increase in
processing power, and each horizontal division a thousandfold increase in memory size. Universal computers can imitate other
entities at their location in the diagram, but the more specialized entities cannot. A 100−million−MIPS computer may be
programmed not only to think like a human, but also to imitate other similarly−sized computers. But humans cannot imitate
100−million−MIPS computers−−our general−purpose calculation ability is under a millionth of a MIPS. Deep Blue's
special−purpose chess chips process moves like a 3−million−MIPS computer, but its general−purpose power is only a thousand
MIPS. Most of the non−computer entities in the diagram can't function in a general−purpose way at all. Universality is an almost
magical property, but it has costs. A universal machine may use ten or more times the resources of one specialized for a task. But if
the task should change, as it usually does in research, the universal machine can be reprogrammed, while the specialized machine
must be replaced.
Extrapolation
By our estimate, today's very biggest supercomputers are within a factor of a hundred of having the power to
mimic a human mind. Their successors a decade hence will be more than powerful enough. Yet, it is unlikely
that machines costing tens of millions of dollars will be wasted doing what any human can do, when they
could instead be solving urgent physical and mathematical problems nothing else can touch. Machines with
human−like performance will make economic sense only when they cost less than humans, say when their
"brains" cost about $1,000. When will that day arrive?
The expense of computation has fallen rapidly and persistently for a century. Steady improvements in
mechanical and electromechanical calculators before World War II had increased the speed of calculation a
thousandfold over hand calculation. The pace quickened with the appearance of electronic computers during
the war−−from 1940 to 1980 the amount of computation available at a given cost increased a millionfold.
Vacuum tubes were replaced by transistors, and transistors by integrated circuits, whose components became
ever smaller and more numerous. During the 1980s microcomputers reached the consumer market, and the
industry became more diverse and competitive. Powerful, inexpensive computer workstations replaced the
drafting boards of circuit and computer designers, and an increasing number of design steps were automated.
The time to bring a new generation of computer to market shrank from two years at the beginning of the
1980s to less than nine months. The computer and communication industries grew into the largest on earth.
Computers doubled in capacity every two years after the war, a pace that became an industry given:
companies that wished to grow sought to exceed it, companies that failed to keep up lost business. In the
1980s the doubling time contracted to 18 months, and computer performance in the late 1990s seems to be
doubling every 12 months.
Faster than Exponential Growth in Computing Power.
The number of MIPS in $1000 of computer from 1900 to the present.
Steady improvements in mechanical and electromechanical calculators before World War II had increased the speed of calculation a
thousandfold over manual methods from 1900 to 1940. The pace quickened with the appearance of electronic computers during the
war, and 1940 to 1980 saw a millionfold increase. The pace has been even quicker since then, a pace which would make humanlike
robots possible before the middle of the next century. The vertical scale is logarithmic, the major divisions represent thousandfold
increases in computer performance. Exponential growth would show as a straight line, the upward curve indicates faster than
exponential growth, or, equivalently, an accelerating rate of innovation. The reduced spread of the data in the 1990s is probably the
result of intensified competition: underperforming machines are more rapidly squeezed out. The numerical data for this power curve
are presented in
the appendix.

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